Facial Emotion Recognition Using a Local Binary Pattern based Deep Learning

Facial-emotion-recognition(FER) is being conducted with the goals of analyzing the psychological characteristics of juvenile offenders and promoting the use of deep learning to the extraction of psychological features. Initially, we talk about how certain personality traits are linked to being good at reading facial expressions. To build a facial emotion identification model from this data, we add more convolutional layers to a CNN and combine it with other neural networks like VGGNet, AlexNet, and LeNet-5. Next, a Central-Local-Binary-Pattern(CLBP) method, which is optimized, is incorporated into the CNN to build a CNN-CLBP framework for FER. After face photos have been preprocessed and key parameters have been optimized, the algorithm is subjected to a validity study. The average detection rate for the CNN-CLBP is 88.16%, which is greater than the recognition rate for other approaches. In addition, image preparation and parameter tuning boost the recognition accuracy of this approach without resorting to poor-tting. In addition, the CNN-CLBP system accurately identifies 97% of pleased and startled expressions, but 22.54% of sad ones. The study's findings can be used as a starting point for further investigations into the mental health of teen offenders.